Sensors or instrumentation deployed in real-world settings for various fields, e.g. analytical medicine, and using various detection modalities, e.g. electroencephalography, sonography, and electrocardiography, usually produce signals corrupted by various types of noise. As a result, noise removal is paramount for accurate data extraction, analysis, and interpretation, as well as efficient storage/transmission. For example, noisy data would make data compression much harder and thus affect storage and transmission, e.g. storing X-rays and mammograms. If not done correctly, the typical preprocessing inherent in any instrument design can actually remove valuable information, and subsequent use of even advanced signal processing methods, no matter how capable, will not be able to recover signal lost by inadequate pre-processing.
An example of a signal processing system and method using both wavelets and neural network processing is disclosed in U.S. Pat. No. 6,763,339. Signal denoising is performed using wavelet processing which incorporates automatic thresholding methods followed by using a single neural network for shape-matching to extract all relevant patterns. In that technique, the neural network processing is performed in the time domain, and a single neural network is used to extract all the patterns and therefore must be pre-trained to recognize all relevant patterns.
Another example of a signal processing system and method using both wavelets and neural network processing is disclosed in published U.S. Patent Application No. US2005/0265629. Extraction of signals is performed using wavelet processing and supervised neural networks such as a modified Logicon projection system. Clean signals, either theoretical behavior derived from the fundamental physics of first principle or noise free signals obtained by other means such as from shielding all the noise from the signal, are used as the targets for training in the neural networks. Artificial noise such as white noise is then injected into the clean signals to produce synthetic noisy inputs. The noisy inputs and the targeted clean signals are then used as input-output pairs under supervision to train the neural network to recognize the signal from the noisy synthetic inputs. As a result, the system is designed based on artificially created signals instead of real signals. Thus obtaining clean signals is needed. In addition to prior knowledge of the ideal clean signals, the system is trained using only a portion of the signals at a time instead of the entire signal to reduce hardware complexity.
Much equipment that generates signals has an expected signal behavior that one can derive from fundamental physics of first principle. For example, the theoretical signals from gas chromatography should be of Gaussian shape. This known ideal behavior of the signal allows supervised training for the neural networks since the target ideal signal has known and established behavior of Gaussian shape. However, there are signals that are of unknown or unspecific nature or shapes because we do not know the theoretical expected behavior. Some examples are the signals from the human brain. Magnetoencephalography or MEG and Electroencephalography or EEG signals from the brain are highly complex and at this junction our understanding of the brain is incomplete and thus we do not have a theoretical expected signal behavior from the brain. As a result, any neural network based on supervised training is useless since we really do not know the real target signal for it cannot be derived from first principle. What is needed therefore is an efficient and broadly based signal denoising and extraction technique applicable to more generalize signals of unknown or unspecific shapes for better signal detection than for specified signals with known shapes such as signals from gas chromatography.
Neurological/mental disorders have received far less attention than traditional illnesses such as stroke, cancer, and diabetes but they are no less disabling. Treating neurological/mental disorders is essentially based on pharmacotherapy and psychotherapy approaches. In certain cases, surgical interventions have been used.
All drugs have undesirable side effects and relative risks. The psychotropic agents used to treat many neurological/mental illnesses belong to a complex and heterogeneous group of compounds notorious for their unpredictable effects. Antidepressants, adrenergic inhibitors, anti-anxiety agents, anti-kindling agents/mood stabilizers, anticonvulsants, and anti-psychotics drugs used for treatment are no exception. For example, the widely prescribed selective serotonin reuptake inhibitors or SSRI antidepressants are considered to be well tolerated; but approximately 25% of depressed patients in the general population will stop treatment due to intolerable side effects. Drug interactions are another important consideration with these medications. SSRI can inhibit the metabolism of many other medications, while anticonvulsants often have the opposite effect, inducing the activity of liver enzymes and accelerating the metabolism of concurrent drugs.
Regarding psychotherapy, methodological studies, which typically examine cognitive or behavioral treatments, indicate that it does help to relieve severity of symptoms. However, there is no consistent information about whether these interventions are helpful in improving other domains of impairment and associated disability, even though these problems are often the greatest concern to patients; nor does the available evidence specify when, and for whom, various psychotherapeutic interventions should be provided, or whether different treatment modalities can and should be combined, or how they should be combined. Thus psychotherapy is as much an art as a science.
Clearly, alternative approaches other than pharmacotherapy, psychotherapy, and surgeries are needed to address these pressing medical problems.
Repetitive Transcranial Magnetic Stimulation (rTMS) has shown some success for treating comorbid PTSD and major depression. rTMS uses very strong magnetic fields, on the order of one Tesla, to stimulate the brain. The exact mechanism of how the magnetic field affects the brain is not fully known. One possibility is that the alternative magnetic field induces eddy current which then stimulates the brain through excitation or inhibitory effects. It is also possible that the magnetic field or more likely the induced eddy current polarizes or depolarizes the neurons. One medical hypothesis is that the higher frequency (>5 Hz) excites the neurons and the lower frequency (<1 Hz) inhibits the neurons and that the combination of the two creates a Ying and Yan effect. However, very little research has been pursued using dual frequencies (low and high), possibly because the size of the coil makes it hard to have two coils operating simultaneously. For most current Transcranial Magnetic Stimulation (TMS) work, the double-loop coil used measures 174 mm—almost the size of a human head, which may make it difficult to localize the stimulated region of the brain.